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#!/usr/bin/python3
# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import StOptReg as reg
import StOptGrids
import StOptGlobal
import Simulators as sim
import Optimizers as opt
import Utils
import dp.DynamicProgrammingByRegressionHighLevel as dyn
import dp.SimulateRegressionControlHighLevel as srtc
import unittest
import importlib
accuracyClose = 1.5
# valorization of a given Lake on a grid
# Gain are proportional to what is withdrawed from the storage
# Only inflows are stochastic
# p_grid the grid
# p_maxLevelStorage maximum level
# p_mesh number of mesh
# p_bCheckClose Do we check if optimization and simulations are close
def lake(p_grid, p_maxLevelStorage, p_mesh, p_bCheckClose):
# test MPI
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
# storage
#########
withdrawalRateStorage = 1000
maturity = 1.
nstep = 10
# number of simulations
nbsimulOpt = 8000
# inflow model
D0 = 50 # initial inflow
m = D0 # average inflow
sig = 5. # volatility
mr = 5. # mean reverting
# a backward simulator
######################
bForward = False
backSimulator = sim.AR1Simulator(D0, m, sig, mr, maturity, nstep, nbsimulOpt, bForward)
# optimizer
##########
storage = opt.OptimizeLakeAR1(withdrawalRateStorage)
# regressor
###########
nbMesh = np.array([], dtype = np.int32)
if p_mesh > 0:
nbMesh = np.zeros(1, dtype = np.int32) + p_mesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = Utils.ZeroPayOff()
# initial values
initialStock = np.zeros(1) + p_maxLevelStorage
initialRegime = 0 # only one regime
# Optimize
fileToDump = "CondExpLakeHL"
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptim = dyn.DynamicProgrammingByRegressionHighLevel(p_grid, storage, regressor , vFunction, initialStock, initialRegime, fileToDump)
nbsimulSim = 8000
bForward = True
forSimulator2 = sim.AR1Simulator(D0, m, sig, mr, maturity, nstep, nbsimulSim, bForward)
storage.setSimulator(forSimulator2)
valSimu2 = srtc.SimulateRegressionControl(p_grid, storage, vFunction, initialStock, initialRegime, fileToDump)
print("valSimu2", valSimu2, "valueOptim", valueOptim)
class testLakeTest(unittest.TestCase):
# linear interpolation
def test_lakeLegendreLinear(self):
# storage
#########
maxLevelStorage = 5000
# grid
######
nGrid = 10
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 1
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
nbmesh = 4
lake(grid, maxLevelStorage, nbmesh, True)
# quadratic interpolation on the basis functions
def test_simpleStorageLegendreQuad(self):
# storage
#########
maxLevelStorage = 5000
# grid
######
nGrid = 5
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 2
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
nbmesh = 4
lake(grid, maxLevelStorage, nbmesh, True)
# forget the AR1 model and suppose that inflows are iid
def test_simpleStorageAverageInflows(self):
# storage
#########
maxLevelStorage = 5000
# grid
######
nGrid = 10
lowValues = np.zeros(1)
step = np.zeros(1) + (maxLevelStorage / nGrid)
nbStep = np.zeros(1, dtype = np.int32) + nGrid
poly = np.zeros(1, dtype = np.int32) + 1
grid = StOptGrids.RegularLegendreGrid(lowValues, step, nbStep, poly)
nbmesh = 0
lake(grid, maxLevelStorage, nbmesh, False)
if __name__ == '__main__':
unittest.main()
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